Identifying Zombie Companies in Finance

Identifying Zombie Companies in Finance

@Farhan7365

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Business #CorporateFinance #MachineLearning #ZombieFirms

Identifying Zombie Companies in Finance

@Farhan73651 week ago

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/p…

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Are you a Zombie? A Supervised Learning Method
to Classify Unviable Firms and Identify the Determi…

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1 Introduction
Long after the global financial crisis, the phenomenon of zombie companies remains …

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Figure 1: Global Zombie Shares. The map shows the presence of zombie companies by
country and visu…

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In particular, we examine empirically the firm-specific determinants of zombie companies in Europe…

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algorithm that allows us to better classify and divide the zombies from the non-zombies
and also f…

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plained by unalike insolvency laws, where stringent insolvency regimes are more efficient
at rehab…

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data set consisting of 62227 observations for 32 European countries and 100250 for the
United Stat…

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is thus classified as zombie if its ICRit is less than one for at least three consecutive years
an…

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Healthy Distressed Zombie Recovered
EU US EU US EU US EU US
Leverage 0.469 0.471 0.633 0.696 0.63…

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Figure 2: Zombie Trend in Europe and Rest of the World. This figure shows the
share of zombie comp…

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On the one hand, a common belief shares the idea that Europe might be a repeat
of Japan’s experien…

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lower productivity growth as capital inflows are directed to unproductive companies
(Gopinath et a…

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Figure 3: Zombie Shares in the United States. The map shows the presence of zombie
companies by st…

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Figure 4: Zombie Shares in Europe. The map shows the presence of zombie companies
by country. The …

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3 Determinants of Zombie Firms
3.1 Decision Trees to Classify Zombies
The high dimensions of our …

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in infinite combinations of sub-rectangles. Therefore, we start with a top-down approach
of binary…

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companies in Europe, indicating that for low values of operating income the tree predicts that the…

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Figure 5: Zombie versus Non-Zombie, Europe 2008-2010. This figure shows the
decision tree for Euro…

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Figure 6: Zombie versus Non-Zombie, Europe 2015-2018. This figure shows the
decision tree for Euro…

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related and debt-related variables are the main drivers categorizing publicly listed European zomb…

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Figure 8: Zombie versus Non-Zombie, United States 2015-2018. This figure shows
the decision tree f…

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zombie companies in Europe during both healthy and crisis time periods. Therefore,
both distressed…

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Figure 9: Distressed versus Non-Distressed, Europe 2008-2010. This figure shows
the decision tree …

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3.3.2 United States
Figure 11 shows the results of the firm-specific determinants of distressed ve…

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Figure 11: Distressed versus Non-Distressed, United States 2008-2010. This
figure shows the decisi…

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3.4 Multi-Class Analysis: Determinants of Zombie, Distressed, Recovered, and Healthy Firms
3.4.1 …

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Figure 13: Zombie, Distressed, Recovered and Healthy, Europe 2008-2010.
This figure shows the mult…

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Figure 14: Zombie, Distressed, Recovered and Healthy, Europe 2015-2018.
This figure shows the mult…

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3.4.2 United States
Figure 15 documents the results of the multi-class tree during the financial c…

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Figure 15: Zombie, Distressed, Recovered, Healthy, United States 2008-2010.
This figure shows the …

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Figure 16: Zombie, Distressed, Recovered, Healthy, United States 2015-2018.
This figure shows the …

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3.5 Benchmark Analysis: Logistic Models
In this section, we analyze the performance of the firm-sp…

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Country Industry
Variables Zombie Distressed Variables Zombie Distressed
Op. Income af ter Depr. …

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Country Industry
Variables Zombie Distressed Variables Zombie Distressed
Stock P rice Low -0.09∗∗…

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4 Conclusion
The zombie phenomenon is not a myth, rather a reality affecting several countries glo…

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Overall, this paper identifies clear differences in classifying zombies in Europe versus
zombies i…

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References
Acharya, Viral V, Matteo Crosignani, et al. (2019). “Zombie Credit and (Dis-)Inflation:…

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Breiman, Leo et al. (1984). Classification and regression trees. CRC press. isbn: 0412048418.
Brou…

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Djankov, Simeon, Caralee McLiesh, and Andrei Shleifer (2007). “Private credit in 129
countries”. I…

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Habib, Ahsan et al. (2020). “Determinants and consequences of financial distress: review
of the em…

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Peters, Ryan H. and Lucian A. Taylor (2017). “Intangible capital and the investment-q
relation”. I…

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A Appendix
A.1 Variables Description
Abbreviation Description
oiadp Operating Income After Depre…

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Variable Definition
Book Leverage = T otal Liabilities / T otal Assets
Net Book Leverage = (T ota…

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Figure A2: Zombie Shares by Industry. The upper chart shows the zombie shares by
industry, GIC gro…

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A.3 Measuring Zombies
The strand of literature focusing on zombie companies provides different app…

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A.4 Decision Tree Example
Let us assume that we want to predict if a person pays back a credit and…

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A.5 Additional Results
Figure A4 shows the firm-specific characteristics of zombie vs. non-zombie …

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Figure A5 shows the firm-specific characteristics of zombie vs. non-zombie in the US using
an alte…

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Figure A6 shows the firm-specific characteristics of zombie vs. non-zombie in Europe in the
upper …

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Figure A7 shows the firm-specific characteristics of distressed vs. non-distressed firms in
Europe…

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Figure A8: Multi-class Decision Tree, United States. The figure shows the results
of the multi-cla…

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Figure A9: Multi-class Decision Tree, Europe. The figure shows the results of the
multi-class tree…

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Identifying Zombie Companies in Finance

@Farhan7365

This paper examines the determinants of zombie companies using a comprehensive dataset of public corporations from Europe and the United States. Zombie firms are defined as those with an interest coverage ratio below one for at least three consecutive years and an age of at least 10 years. The study employs decision trees, a supervised learning method, to identify the main drivers classifying zombie companies.

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#CorporateFinance #MachineLearning #ZombieFirms

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